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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是1711-1720 订阅

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Performance Analysis of Students by Evaluating Their Examination Answer Scripts by Using Soft Computing Techniques 3rd

Performance Analysis of Students by Evaluating Their Examina...

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3rd international conference on Emerging Research in Electronics, computer Science and Technology (ICERECT)

作者： Bhanuprakash, C. Nijagunarya, Y. S. Jayaram, M. A. Siddaganga Inst Technol Dept Master Comp Applicat Tumakuru India Siddaganga Inst Technol Dept Comp Sci & Engn Tumakuru India

ISBN: (纸本)9789811358029;9789811358012

The theme of this paper is to analyze how a student can perform in an examination by answering the questions by selecting from various units. This paper focuses mainly on the common units of the questions to which students can make their maximum attempts to write the answers and the units to which the students will make rare attempts. It is considered to be one of the attempts made in this regard which will enlighten teaching community as well as college management to focus where exactly risks will be there to both students and teachers. From last 4 years, AICTE has made mandatory rules to all the technical education institutes in this regard as a part of accreditation certificate. It helps in framing the syllabus as well as setting the question papers by considering number of chapters for a given unit and number of units for a given subject. The main theme behind this performance analysis lies with the units with which maximum students will make an attempt to answer which in turn improve the confidence level of the teacher in setting the type of questions and framing trickiness in the questions. Here, there were two approaches which have been suggested both in traditional manual method and soft computing method. In the manual method, a program application was written which makes the group of the students based on the total marks scored by the students. In soft computing method, it was used with the basic feed-forward neural network to group the students by taking the split marks of individual units of the student. These splits marks will be considered as features to train the network. At the end, a fruitful positive result was received in both manual methods and soft computing methods.

关键词： Grouping Feed-forward neural network Range of values Neural networks machine learning Support vector machines

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Detecting Anomalies in application Performance Management System with machine learning Algorihms

Detecting Anomalies in Application Performance Management Sy...

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Electronic Information Technology and computer Engineering (EITCE), international conference on

作者： Yujun Shi Kehua Miao Department of Automation Xiamen University Xiamen China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

application performance management which is short for APM is a real-time monitoring of enterprise systems to implement a systematic solution to application performance management and fault management. APM is a relatively new network management direction. It mainly refers to monitoring and optimizing key business applications of enterprises, improving the reliability and quality of enterprise applications, ensuring users to get good services and reducing total cost of ownership (TCO). If a company's business-critical applications are powerful, they can increase competitiveness and achieve business success. Therefore, enhancing application performance management (APM) can generate huge business benefits. This paper explores the application of machine learning-based anomaly detection algorithm in APM system by analyzing continuous time series data from actual network systems.

关键词： System performance Forestry machine learning Anomaly detection Monitoring Support vector machines Data preprocessing

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Heterotropia diagnosis with smartphone using machine learning and computer vision 3

Heterotropia diagnosis with smartphone using machine learnin...

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3rd international conference on Contemporary Computing and Informatics, IC3I 2018

作者： Gupta, Harsh Moder School Department of Computer Science New Delhi India

ISBN: (纸本)9781538668948

In this paper, a deviation-based algorithm is proposed which exploits the power of TensorFlow, OpenCV, and machine learning to diagnose four forms of heterotropia - esotropia, exotropia, hypertropia and hypotropia. The mechanism developed for lateral, vertical, and angular deviation has been tested for K-Nearest-Neighbors (KNN), Support Vector machine (SVM), and a trained neural network, based on 6 features. Finally, the accuracy, specificity, and sensitivity of the 2 MLC and convolutional neural network (CNN) was tested in a study of 337 users with and without heterotropia. Eyeris, an Android based application, has been developed as a proof of concept for this paper. The results yielded highest accuracy for SVM-based classification. © 2018 IEEE.

关键词： computer vision

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Chatbot for Disease Prediction and Treatment Recommendation using machine learning

Chatbot for Disease Prediction and Treatment Recommendation ...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Rohit Binu Mathew Sandra Varghese Sera Elsa Joy Swanthana Susan Alex Department of Computer Science and Engineering Saintgits College of Engineering Kottayam Kerala India

Hospitals are the most widely used means by which a sick person gets medical check-ups, disease diagnosis and treatment recommendation. This has been a practice by almost all the people over the world. People consider it as the most reliable means to check their health status. The proposed system is to create an alternative to this conventional method of visiting a hospital and making an appointment with a doctor to get diagnosis. This research intends to apply the concepts of natural language processing and machine learning to create a chatbot application. People can interact with the chatbot just like they do with another human and through a series of queries, chatbot will identify the symptoms of the user and thereby, predicts the disease and recommends treatment. This system can be of great use to people in conducting daily check-ups, makes people aware of their health status and encourages people to make proper measures to remain healthy. According to this research, such a system is not widely used and people are less aware of it. Executing this proposed framework can help people avoid the time-consuming method of visiting hospitals by using this free of cost application, wherever they are.

关键词： Diseases Natural language processing Medical diagnostic imaging Hospitals

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Mobile application Based Cataract Detection System

Mobile Application Based Cataract Detection System

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Vaibhav Agarwal Vaibhav Gupta Vivasvan Manasvi Vashisht Kiran Sharma Neetu Sharma Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Allied Science Graphic Era Deemed to be University Dehradun Uttarakhand India

Cataract is one of the most common diseases that is experienced by human beings when they grow old. Cataract is a situation where we experience the formation of cloud on the lens of our eyes. This often leads to a decrease in vision and leads to vision impairment. The developing speed of cataract is not very fast. It is a slow process. It can affect single or both eyes. The main symptoms that this disease shows up are blurry vision, faded colors, trouble while seeing in bright light, etc. most often these symptoms result in trouble doing many activities. 33% of the people worldwide are suffering from this disease. Now the detection of disease is a tedious task. Most of the people who are suffering from this disease have to consult a doctor in order to detect the presence of this disease. This process is time-consuming and costly as well. In this paper, a method is proposed and implemented, a smartphone based android application is developed using the proposed methodology that can be used to detect the presence of the cataract in an individual's eye. The developed application is based upon Android architecture and can be used only on Android phones. machine learning and image processing techniques are used to develop the proposed methodology. This system has been trained on a number of data sets in order to improve its accuracy and have resulted in the successful completion of this research.

关键词： Cataracts Lenses Diseases machine learning machine learning algorithms Smart phones Proteins

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Interaction Behavior of Older Adults with Immersive Virtual Reality application for Cognitive Training 3

Interaction Behavior of Older Adults with Immersive Virtual ...

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3rd international conference on computer and Communication Systems (ICCCS)

作者： Intraraprasit, Monthon Sunhem, Wisuwat Jinjakam, Chompoonuch King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Comp Engn Bangkok 10520 Thailand

ISBN: (纸本)9781538663509

Nowadays, older adults face health problems. The main problem is decreasing our brain skills. Memory, visuospatial skill, and cognitive functions are considered to be essence of the brain skills. These capabilities can performance by applying cognitive training or brain training. Currently, virtual reality (VR) technology is applied to cognitive training application. The results of training can be analyzed after several weeks, but it is a lack of interaction behavior analysis of older adults with VR application. The behavior analysis helps to design efficient program training, in other words, we can utilize better VR application with older adults who do not familiar with current technology. Moreover, we can understand behavior of older adult via VR application for their brain ability. VR application can be designed and kept log files for interaction behavior analysis. The algorithm for preliminary analysis is machine learning. machine learning can predict score that measures brain's ability from the dataset. We adopted 2 models in this research. The first model is to predict the scores of visual short-term memory, called VSTM-model. The second model is to predict the scores of visuospatial skill, called VS-model. We performed baseline regression and support vector regression algorithm for score prediction of behavior. Root mean squared error is selected to measure performance of the algorithm;root mean squared error of baseline regression equal to 0.3012 in VSTM-model and 1.2427 in VS-model, and root mean squared error of support vector regression equal to 0.2876 in VSTM-model and 1.0536 in VS-model.

关键词： human computer interaction cognitive training machine learning virtual reality application behavior analysis

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Proposed model on Prediction and Analysis using application of Health care 3

Proposed model on Prediction and Analysis using application ...

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3rd IEEE international conference on Communication and Electronics Systems (ICCES)

作者： Khan, Shamsuddin S. Barve, Rohit Kulkarni, Umesh St Francis Inst Technol Comp Engn Mumbai Maharashtra India Vidyalankar Inst Technol Informat Technol Mumbai Maharashtra India Vidyalankar Inst Technol Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781538647653

Proposed system does analysis and prediction of with Regression Analysis. Conceptually the Intelligence is a combination of technologies and processing (i.e. Virtualization, Cloud Computing, and machine learning etc.). Now a day machine learning (ML) is one of the fastest growing fields in computer science and health care informatics. ML's goal is to develop algorithm which can learn as time passes and improve also and can be used for prediction algorithm that can interact with agent and can optimize their learning behavior through these interactions where the agent can also be a human. This paper discusses about working application of Pseudo intelligence using health care. The result of pseudo intelligence improves the efficiency in providing better survivability of people. Intelligence system is helpful to find out need requirement of a particular health care. Simple linear multivariate regression technique is used for prediction. Dataset is taken from UCI repository. We have achieved maximum accuracy using regression. Aggregation is used to convert uncertain probabilistic data into a certain data. Data mining is used for future power requirement analysis.

关键词： Regression Probabilistic data Health Informatics Cognitive learning Cloud Computing

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machine learning based Health Prediction System using IBM Cloud as PaaS

Machine Learning based Health Prediction System using IBM Cl...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Asif Ahmed Neloy Sazid Alam Rafia Alif Bindu Nusrat Jahan Moni dept. of electrical and computer engineering North South University Dhaka Bangladesh

Adaptable Critical Patient Caring system is a key concern for hospitals in developing countries like Bangladesh. Most of the hospital in Bangladesh lack serving proper health service due to unavailability of appropriate, easy and scalable smart systems. The aim of this project is to build an adequate system for hospitals to serve critical patients with a real-time feedback method. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing critical patient's health condition with machine learning and IBM cloud computing as Platform as a service (PaaS). machine learning (ML) based health prediction of the patients is the key concept of this research. IBM Cloud, IBM Watson studio is the platform for this research to store and maintain our data and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For improving the accuracy of the model, the bagging method of ensemble learning has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have developed a mobile application named “Critical Patient Management System - CPMS” for real-time data and information view. The system architecture is designed in such a way that the ml models can train and deploy in a real-time interval by retrieving the data from IBM Cloud and the cloud information can also be accessed through CPMS in a requested time interval. To help the doctors, the ml models will predict the condition of a patient. If the prediction based on the condition gets worse, the CPMS will send an SMS to the duty doctor and nurse for getting immediate attention to the patient. Combining with the ml models and mobile application, the project may serve as a smart healthcare solut

关键词： Cloud computing Medical services machine learning Monitoring Predictive models Bagging Computational modeling

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Research on Radar Plot Classification Based on Fully Connected Neural Network

Research on Radar Plot Classification Based on Fully Connect...

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Electronic Information Technology and computer Engineering (EITCE), international conference on

作者： Yongmei Qi Chengzhi Yu Xiao Dai The 723 Institute CSIC Yangzhou China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

Clutter is the inherent environment of radar signal detection and processing. In this paper, the full connected neural network is used to study the classification of radar clutter and real targets. First, according to the engineering experience, the multi-dimensional feature index of radar plot is given, and the fully connected neural network is built in the Tensorflow architecture. According to the application scenarios in this paper, the effect evaluation indexes of the training model were defined, and the classical loss function was optimized. Finally, the training results were given.

关键词： Clutter Radar Biological neural networks Data models Training machine learning

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Proceedings - 3rd international conference on Information Systems and computer Science, INCISCOS 2018

Proceedings - 3rd International Conference on Information Sy...

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3rd international conference on Information Systems and computer Science, INCISCOS 2018

ISBN: (纸本)9781538676127

The proceedings contain 53 papers. The topics discussed include: noise reduction and delineation of ECG signal;exoskeleton prototype to mitigate pronosupination tremors in people with Parkinson's disease;a sliding mode control for a planar 4-cable direct driven robot;integral-optimal sliding mode control for vertical take-off and landing system;trajectory tracking control for aerial manipulator based on Lyapunov and sliding mode control;mobile application for ergonomic analysis of the sitting posture of the torso;robust MPC tuning by quadratic weights online estimation of the cost function through extended kalman filter;a linear algebra controller approach for systems with inverse response;and air quality monitoring intelligent system using machine learning techniques.

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